Quantifying fecal and plasma short-chain fatty acids in healthy Thai individuals.

Short-chain fatty acids (SCFAs) are involved in important physiological processes such as gut health and immune response, and changes in SCFA levels can be indicative of disease. Despite the importance of SCFAs in human health and disease, reference values for fecal and plasma SCFA concentrations in healthy individuals are scarce. To address this gap in current knowledge, we developed a simple and reliable derivatization-free GC-TOFMS method for quantifying fecal and plasma SCFAs in healthy individuals. We targeted six linear- and seven branched-SCFAs, obtaining method recoveries of 73-88% and 83-134% in fecal and plasma matrices, respectively. The developed methods are simpler, faster, and more sensitive than previously published methods and are well suited for large-scale studies. Analysis of samples from 157 medically confirmed healthy individuals showed that the total SCFAs in the feces and plasma were 34.1 ± 15.3 µmol/g and 60.0 ± 45.9 µM, respectively. In fecal samples, acetic acid (Ace), propionic acid (Pro), and butanoic acid (But) were all significant, collectively accounting for 89% of the total SCFAs, whereas the only major SCFA in plasma samples was Ace, constituting of 93% of the total plasma SCFAs. There were no statistically significant differences in the total fecal and plasma SCFA concentrations between sexes or among age groups. The data revealed, however, a positive correlation for several nutrients, such as carbohydrate, fat, iron from vegetables, and water, to most of the targeted SCFAs. This is the first large-scale study to report SCFA reference intervals in the plasma and feces of healthy individuals, and thereby delivers valuable data for microbiome, metabolomics, and biomarker research.

GC × GC-TOFMS metabolomics analysis identifies elevated levels of plasma sugars and sugar alcohols in diabetic mellitus patients with kidney failure.

Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices.

CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics.

Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is currently a challenge in GC × GC-TOFMS data analysis. We present an open-source deep learning (DL) software called contour regions of interest (ROI) identification, simulation and untargeted metabolomics profiler (CRISP). CRISP integrates multiple customizable deep neural network architectures for assisting the semi-automated identification of ROIs, contour synthesis, resolution enhancement and classification of GC × GC-TOFMS-based contour images. The approach includes the novel aggregate feature representative contour (AFRC) construction and stacked ROIs. This generates an unbiased contour image dataset that enhances the contrasting characteristics between different test groups and can be suitable for small sample sizes. The utility of the generative models and the accuracy and efficacy of the platform were demonstrated using a dataset of GC × GC-TOFMS contour images from patients with late-stage diabetic nephropathy and healthy control groups. CRISP successfully constructed AFRC images and identified over five ROIs to create a deepstacked dataset. The high fidelity, 512 × 512-pixels generative model was trained as a generator with a Fréchet inception distance of <47.00. The trained classifier achieved an AUROC of >0.96 and a classification accuracy of >95.00% for datasets with and without column bleed. Overall, CRISP demonstrates good potential as a DL-based approach for the rapid analysis of 4-D GC × GC-TOFMS untargeted metabolite profiles by directly implementing contour images. CRISP is available at https://github.com/vivekmathema/GCxGC-CRISP.

High Levels of Serum IgG for Opisthorchis viverrini and CD44 Expression Predict Worse Prognosis for Cholangiocarcinoma Patients after Curative Resection.

BACKGROUND: Opisthorchis viverrini (OV)-associated cholangiocarcinoma (CCA) has a high immune response with chronic inflammation and oxidative stress. CD44 and Nestin, two cancer stem cell (CSC) markers, play major roles in cancer cell survival. Effects of immune response and expression CSC markers on survival of patients with CCA remain unclear. OBJECTIVE: To investigate the effects of level of OV IgG together with CSC marker expression and also the combination of these markers on survival of CCA patients after curative resection. METHODS: All serum specimens from CCA patients who underwent curative surgery from 2005 to 2015 were examined for IgG for OV antigen by ELISA. Tissue specimens were studied for CD44 and Nestin expression. Survival analysis by Cox proportional hazard model was used for estimating hazard ratio (HR) with a 95% confidence interval (CI). RESULTS: In this study, 122 (69.3%) of 176 were positive for OV IgG, and 35 (19.9%) were considered to have high-positive OV IgG. CD44s positive expression was found in 54 (40%), CD44v6 high expression in 96 (69.6%), CD44v8-10 high expression in 87 (63.5%) and Nestin high expression in 21 (16.1%). Multivariate survival analysis found that high-positive OV IgG and late stage tumor were independent prognostic factors with the adjusted HR of 2.24 (95% CI 1.27-3.93) and 2.78 (95% CI 1.46-5.29), respectively. Subgroup analysis in early and late stage CCA showed that a combined positive OV IgG and CD44s expression with the high expression of CD44v8-10 had the significantly poorest prognosis with HR of 3.75 (95% CI 1.61-8.72) and HR of 1.76 (95% CI 1.02-3.03), respectively. CONCLUSION: A high level of OV IgG as well as a high level of CSC markers resulted in an aggressive CCA. OV IgG level together with CSC markers can be used as the prognostic markers for CCA patients' survival. The study of the CD44 pathway is promising for adjuvant treatment.

Urine proteomics study reveals potential biomarkers for the differential diagnosis of cholangiocarcinoma and periductal fibrosis.

Cholangiocarcinoma (CCA) is a primary malignant tumor of the epithelial lining of biliary track associated with endemic Opisthorchis viverrini (Ov) infection in northeastern Thailand. Ov-associated periductal fibrosis (PDF) is the precancerous lesion for CCA, and can be detected by ultrasonography (US) to facilitate early detection. However, US cannot be used to distinguish PDF from cancer. Therefore, the objective of this study was to discover and qualify potential urine biomarkers for CCA detection in at-risk population. Biomarker discovery was conducted on pooled urine samples, 42 patients per group, with PDF or normal bile duct confirmed by ultrasound. After depletion of high abundance proteins, 338 urinary proteins were identified from the 3 samples (normal-US, PDF-US, CCA). Based on fold change and literature review, 70 candidate proteins were selected for qualification by multiple reaction monitoring mass spectrometry (MRM-MS) in 90 individual urine samples, 30 per group. An orthogonal signal correction projection to latent structures discriminant analysis (O-PLS-DA) multivariate model constructed from the 70 candidate biomarkers significantly discriminated CCA from normal and PDF groups (P = 0.003). As an independent validation, the expression of 3 candidate proteins was confirmed by immunohistochemistry in CCA tissues: Lysosome associated membrane glycoprotein 1 (LAMP1), lysosome associated membrane glycoprotein 2 (LAMP2) and cadherin-related family member 2 (CDHR2). Further evaluation of these candidate biomarkers in a larger cohort is needed to support their applicability in a clinical setting for screening and monitoring early CCA and for CCA surveillance.

Discovery and Qualification of Serum Protein Biomarker Candidates for Cholangiocarcinoma Diagnosis.

Cholangiocarcinoma (CCA) is a major health problem in northeastern Thailand. The majority of CCA cases are clinically silent and difficult to detect at an early stage. Although abdominal ultrasonography (US) can detect premalignant periductal fibrosis (PDF), this method is not suitable for screening populations in remote areas. With the goal of developing a blood test for detecting CCA in the at-risk population, we carried out serum protein biomarker discovery and qualification. Label-free shotgun proteomics was performed on depleted serum samples from 30 participants (n = 10 for US-normal, US-PDF, and CCA groups). Of 40 protein candidates selected using multiple reaction monitoring on 90 additional serum samples (n = 30 per group), 11 discriminatory proteins were obtained using supervised multivariate statistical analysis. We further evaluated 3 candidates using ELISA and immunohistochemistry (IHC). S100A9, thioredoxin (TRX), and cadherin-related family member 2 (CDHR2) were significantly different between CCA and normal, and CCA and PDF groups when measured in an additional 247 serum samples (P < 0.0001). By IHC, TRX and CDHR2 were detected in the cytoplasm and nucleus of CCA and inflammatory cells. S100A9 was detected in the infiltrating tumor stroma immune cells. Proteomics discovery and qualification in depleted sera revealed promising biomarker candidates for CCA diagnosis.

BMP-7 blocks the effects of TGF-ß-induced EMT in cholangiocarcinoma.

Epithelial-mesenchymal transition (EMT) is characterized by the loss of epithelial markers and the gain of mesenchymal markers. EMT is believed to be a major mechanism supporting cancer cell metastasis. The activation of EMT can be induced by various types of inflammatory cytokines including transforming growth factor ß (TGF-ß) whereas bone morphogenetic protein-7 (BMP-7) can inhibit this process. In this study, the up-regulation of Twist transcription factor and N-cadherin, mesenchymal marker in CCA tissues, has been demonstrated and it has been found that the high expression of Twist was significantly associated with poor prognosis of CCA patients (P = 0.010). Moreover, CCA samples showing Twist nuclear expression were significantly correlated with the up-regulation of N-cadherin (P = 0.024). These results also showed that the inflammatory mediator TGF-ß induces CCA cell migration, one of the metastatic processes possibly via stimulation of Twist, N-cadherin and vimentin expression. Additionally, it has been shown that BMP-7 inhibits TGF-ß-induced CCA cell migration, through inhibition of TGF-ß-mediated Twist and N-cadherin expressions. These data reinforce the rationale to use BMP-7 as an EMT inhibitor to suppress the progression of CCA and might be a therapeutic approach to improve efficiency for CCA treatment.

Tumor necrosis factor-α modulates epithelial mesenchymal transition mediators ZEB2 and S100A4 to promote cholangiocarcinoma progression.

BACKGROUND: The epithelial-mesenchymal transition (EMT) process strongly contributes to cancer metastasis. This study was to investigate the alteration of EMT-related proteins (ZEB1, ZEB2 and S100A4) in cholangiocarcinoma (CCA) tissues. The effect of tumor necrosis factor-α (TNF-α) on the expression of those molecules in CCA cells was investigated. METHODS: The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay was used to quantify ZEB1, ZEB2 and S100A4 mRNA levels in 50 CCA tissues and related its expression to clinicopathological data. ZEB2 protein immunostaining was investigated in 165 CCA tissues. The effect of TNF-α on EMT-related CCA cell migration was evaluated using qRT-PCR, immunofluorescence and transwell migration assays. RESULTS: ZEB2 and S100A4 mRNA levels were found to be higher in CCA tissues. High levels of S100A4 mRNA and ZEB2 protein were significantly associated with CCA metastasis (P = 0.04 and P = 0.03). Moreover, a trend toward statistical association was found with high levels of both ZEB2 mRNA and protein with shorter survival time (P = 0.10 and P = 0.19). In addition, TNF-α induced CCA cell migration by the induction of transforming growth factor-ß (TGF-ß) resulting in ZEB2 and S100A4 mRNA and protein activation. CONCLUSIONS: These studies demonstrate that TNF-α plays crucial role in the progression of CCA by activating TGF-ß signaling and the induction of ZEB2 and S100A4, EMT-related proteins expression.